Recently contrastive learning has emerged as a powerful self-supervised learning technique and has shown great promise and achieved state-of-the-art results for a variety of different tasks [1]}, [2]}, [3]}, [4]}. In particular, a more recently proposed contrastive method called SimCLR [5]} provides a generalized learning framework that does not requires any specialized architecture [4]}, [7]} nor any memory bank [8]}, [9]}, [10]}. SimCLR uses data augmentation to obtain two representations of the same input which they consider as positive pairs. SimCLR learns the visual representation by maximizing the agreement of positive pairs via a contrastive loss in the latent space.
